The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples.
Subtitles in English and Spanish.

Linear Regression - Machine Learning Fun and Easy
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Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel
Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
Dependent Variable – Variable who’s values we want to explain or forecast
Independent or explanatory Variable that Explains the other variable. Values are independent.
Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents.
And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways
Used for 2 Applications
To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables-
• To see how increase in sin tax has an effect on how many cigarettes packs are consumed
• Sleep hours vs test scores
• Experience vs Salary
• Pokemon vs Urban Density
• House floor area vs House price
Forecast new observations – Can use what we know to forecast unobserved values
Here are some other examples of ways that linear regression can be applied.
• So say the sales of ROI of Fidget spinners over time.
• Stock price over time
• Predict price of Bitcoin over time.
Linear Regression is also known as the line of best fit
The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x
You most likely learnt this in school.
So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis.
M is your slope or gradient, if you change this, then your line rotates along the intercept.
Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression
Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series.
So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e
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#Statistics
#Regression
#Linear Regression
#Classical Approach
#Finding Coefficients and Lines of Regression
Case
5. Two judges A and B have given marks to 7 plays independently.
Sr. No. 1 2 3 4 5 6 7
Marks
given by
A 46 42 44 40 43 41 45
B 40 38 36 35 39 37 41
At the time of the 8th play B could not remain present and A has given 40 marks to the 8th play. Estimate the marks B would have given to the 8th play had he been present.
This is one sided case. In real life we mostly we have to face this kind of problems where we have a value of a variable known and we need to find the corresponding estimated value of the other variable.
In such case the variable for which we need to find the estimated value becomes the dependent variable and the other one independent variable. And also we need to find out one one coefficient of regression and only one line of regression, both of the dependent variable on the independent variable.
In this case also we have used the formula in which we need to substitute the sum totals based on the deviations taken from means.
Regression, Linear Regression, Coefficients of Regression, Lines of Regression, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12
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In this video we detail how to calculate the coefficients for a multiple regression. In particular, we detail how to calculate the slope and intercept coefficients for the case of two independent variables and a single dependent variable.

Provides an example of student college application for carrying out logistic regression analysis with R.
Data: https://goo.gl/VEBvwa
R File: https://goo.gl/PdRktk
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- use of a categorical binary output variable
- data partition
- logistic regression model
- prediction
- equation for prediction
- misclassification errors for training and test data
- confusion matrix for training and test data
- goodness-of-fit test
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Learn how to make predictions using Simple Linear Regression. To do this you need to use the Linear Regression Function (y = a + bx) where "y" is the dependent variable, "a" is the y intercept, "b" is the slope of the regression line, and "x" is the independent variable.
This video also shows you how to determine the slope (b) of the regression line, and the y intercept (a).
In order to determine the slope of a line you will need to first determine the Pearson Correlation Coefficient - this is described in a separate video (https://www.youtube.com/watch?v=2SCg8Kuh0tE).

Decision Tree (CART) - Machine Learning Fun and Easy
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Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression (CART).
So a decision tree is a flow-chart-like structure, where each internal node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node.
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Predict who survives the Titanic disaster using Excel.
Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?"
Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate.
How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.

Tutorial on how to calculate Multiple Linear Regression using SPSS. I show you how to calculate a regression equation with two independent variables. I also show you how to create a Pearson r correlation matrix using output from SPSS.
Playlist on Using SPSS For Multiple Linear Regression
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL
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This video will show you how to make a simple scatter plot. Remember to put your independent variable along the x-axis, and you dependent variable along the y-axis. For more videos please visit http://www.mysecretmathtutor.com

Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/
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Quantitative researchers measure variables to answer their research question.
The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information.
In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level.
To remember these levels of measurement in order use the acronym NOIR or noir.
The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories.
The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair.
Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories.
Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical.
Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order.
While there is an order, it is also unknown how much distance is between each category.
Values in an ordinal scale simply express an order.
All nominal level tests can be run on ordinal data.
Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured.
To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode.
Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known.
Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement.
For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81.
If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy.
Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero.
Typically this level of measurement is only possible with physical measurements like height, weight and length.
Any statistical tests can be used with ratio level data as long as it fits with the study question and design.

LearnAnalytics demonstrates use of Multiple Linear Regression on Excel 2010. (Data Analysis Toolpak). Data set referenced in video can be downloaded at www.learnanalytics.in/blog/wp-content/uploads/2014/02/car_sales.xlsx

Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
-------------------------
Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Provides steps for carrying out linear discriminant analysis in r and it's use for developing a classification model. Includes,
- Data partitioning
- Scatter Plot & Correlations
- Linear Discriminant Analysis
- Stacked Histograms of Discriminant Function Values
- Bi-Plot interpretation
- Partition plots
- Confusion Matrix & Accuracy - training & testing data
- Advantages and disadvantages
linear discriminant analysis is an important statistical tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

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In this lesson, the student will learn the concept of a random variable in statistics. We will then use the idea of a random variable to describe the discrete probability distribution, which is a key idea used to solve statistics problems.

Overview of multiple regression including the selection of predictor variables, multicollinearity, adjusted R-squared, and dummy variables.
If you find these videos useful, I hope that you will consider signing up for my online statistics workshop on Udemy, which contains additional videos and lots of problems to help you apply and reinforce the important concepts: https://www.udemy.com/statshelp/?couponCode=coefficient

Explained K means Clustering Algorithm With Best Example In Quickest And Easiest way Ever in Hindi.
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In this video, I present an example of a multiple regression analysis of website visit duration data using both quantitative and qualitative variables. Variables used include gender, browser, mobile/non-mobile, and years of education. Gender and mobile each require a single dummy variable, while browser requires several dummy variables. I also present models that include interactions between the dummy variables and years of education to analyze intercept effects, slope effects, and fully interacted models. In short, I cover:
- multiple category qualitative variables
- dummy variables
- intercept effects
- slope effects
- dummy interactions
I hope you find it useful! Please let me know if you have any questions!
--Dr. D.

short introduction on Association Rule with definition & Example, are explained.
Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database.
Parts of Association rule is explained with 2 measurements support and confidence.
types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples.
Names of Association rule algorithm and fields where association rule is used is also mentioned.

This lesson will teach you Predictive analytics and Predictive Modelling Techniques.
Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE
After completing this lesson you will be able to:
1. Understand regression analysis and types of regression models
2. Know and Build a simple linear regression model
3. Understand and develop a logical regression
4. Learn cluster analysis, types and methods to form clusters
5. Know more series and its components
6. Decompose seasonal time series
7. Understand different exponential smoothing methods
8. Know the advantages and disadvantages of exponential smoothing
9. Understand the concepts of white noise and correlogram
10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc
11. Understand all the analysis techniques with case studies
Regression Analysis:
• Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables.
• It predicts the value of a dependent variable based on one or more independent variables
• Coefficient explains the impact of changes in an independent variable on the dependent variable.
• Widely used in prediction and forecasting
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The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice.
Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.
As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice.
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Logistic regression is the appropriate regression analysis to conduct when the dependent variable is dichotomous (binary). Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.
Sometimes logistic regressions are difficult to interpret; the Intellectus Statistics tool easily allows you to conduct the analysis, then in plain English interprets the output.
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This Decision Tree in R tutorial video will help you understand what is decision tree, what problems can be solved using decision trees, how does a decision tree work and you will also see a use case implementation in which we do survival prediction using R. Decision tree is one of the most popular Machine Learning algorithms in use today, this is a supervised learning algorithm that is used for classifying problems. It works well classifying for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets based on the most significant attributes/ independent variables. In simple words, a decision tree is a tree-shaped algorithm used to determine a course of action. Each branch of the tree represents a possible decision, occurrence or reaction. Now let us get started and understand how does Decision tree work.
Below topics are explained in this Decision tree in R tutorial :
1. What is Decision tree?
2. What problems can be solved using Decision Trees?
3. How does a Decision Tree work?
4. Use case: Survival prediction in R
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A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls
In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test.
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All of the images are licensed under creative commons and public domain licensing:
1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg
Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg
Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg
pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg
The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php

Learn About Statistical Modeling in R at https://www.datacamp.com/courses/statistical-modeling-in-r-part-2
When you have a model with multiple explanatory variables, there can be a lot to keep track of. For instance, to evaluate the model you're going to need to set a value for each of the explanatory variables, this in turn involves figuring out what a relevant value is. None of this is hard. For instance, it's often appropriate to use the mean or median of a quantitative variable or the most common group of a categorical variable. Still, it is tedious.
If you want to make a graphic showing the form of the model function, there's even more to do, since you'll want to select several different levels of each explanatory variable to include in the plot.
To streamline the task, the statisticalModeling package provides two helper functions.
effect_size() calculates an effect size for you. The main advantage here is that effect_size() scans your data to find appropriate values for the explanatory variables and an appropriate step size for the explanatory variable whose effect size you are looking for. The function takes two arguments: the model you are examining and a very short formula with just one explanatory variable on the right-hand side of the tilde. This identifies the variable whose effect size you're looking for.
Later on in the course, you'll see another advantage of effect_size(): it lets you find a confidence interval.
The other helper function is plot_model(). This graphs model functions.
[[3.09]] As an example, consider a model of whether a worker is married based on their education, sector of employment, sex, and experience on the job.
The conventional format for graphing a model is puts the response variable on the y-axis. In this model, that's the probability of being married. The most important explanatory variable goes the x-axis. Of course, what's "most important" depends on your purpose in constructing and displaying the model. I've selected the age variable. If there are other variables you want to display, you can use color, as I've done here with sex, and you can "facet" the graph, making small subgraphs. Here, I'm showing two different levels of education in the columns and three different employment sectors in the three rows of facets. So, in this graph, there are four different explanatory variables. You can judge for yourself whether this is overwhelming --- you don't want to make the graphic so busy that it becomes difficult to interpret.
The graphs show that older workers are more likely to be married; that's the general upward slope in the curves of probability of being married vs age. You can also see that there are some systematic differences in marriage probabilities for males and for females. For professional sector workers, males are more likely to be married than females. The opposite is true for clerical workers. For service workers, the probability is about the same for both sexes. Females with more education are less likely than males to be married in the professional sector.
Each of the sub-graphs is created by setting the education and sector variables to the level indicated in the margin. Then the model is evaluated for each sex at each of many ages. You can, of course, do this directly with predict(). The plot_model() function does this work for you, collects the results, and graphs them.
Let's talk about how to design such graphs. [[3.11]]
First, the response variable will always be on the y-axis. This honors convention and helps the viewer of the graph to orient him or herself. If the response is categorical, we'll plot the probability of the first level.
For the x-axis, choose the explanatory variable whose effect you want to highlight.
For a quantitative variable on the x-axis, the effect size is the rate of change of the plotted line, in other words, the slope of the line.
For categorical variables, the effect size is the difference in model output between two different levels.
Additional explanatory variables are used for color and for faceting the graphic, that is, subplots for different values. To specify which explanatory variable plays which role, you use a formula with the explanatory variables on the right-hand side. The first variable in the formula will go on the x-axis. If there is a second variable included, it will be displayed as color. If there are third or fourth variables, they define the facets.

An explanation of how to compute the chi-squared statistic for independent measures of nominal data.
For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html
There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html

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Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
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This video explains what is meant by the covariance and correlation between two random variables, providing some intuition for their respective mathematical formulations. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

An example of how to calculate linear regression line using least squares. A step by step tutorial showing how to develop a linear regression equation. Use of colors and animations.
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Levels of measurement can be split into two groups: qualitative and quantitative data. They are very intuitive, so don’t worry.
Qualitative data can be nominal or ordinal.
Nominal variables are like the categories we talked about just now – Mercedes, BMW or Audi, or like the four seasons – winter, spring, summer and autumn. They aren’t numbers and cannot be put in any order.
Ordinal data, on the other hand, consists of groups and categories but follows a strict order. Imagine you have been asked to rate your lunch and the options are: disgusting, unappetizing, neutral, tasty, and delicious. Although we have words and not numbers, it is obvious that these preferences are ordered from negative to positive, thus the data is qualitative, ordinal.
Okay, so what about quantitative variables? Well, as you may have guessed by now, they are also split into two groups: interval and ratio.
Intervals and ratios are both represented by numbers but have one major difference. Ratios have a true zero and intervals don’t.
For example, length is a ratio variable. You all know that 0 inches or 0 feet means that there is no length.
With temperature, however, we have a different story. It is usually an interval variable. Let me explain. Usually, it is expressed in Celsius or Fahrenheit. They are both interval variables. 0 degrees Celsius or 0 degrees Fahrenheit don’t not mean anything, as the absolute zero temperature is actually -273.15 degrees Celsius, or -459.67 degrees Fahrenheit.
However, we can easily say that 80 degrees Fahrenheit is less than 100 degrees Fahrenheit. In the case of interval variables, the difference is meaningful, but the 0 is not.
Continuing this temperature example, there is another scale – Kelvin’s. According to it, the absolute minimum temperature is 0 degrees Kelvin. Therefore, if the degrees are stated in Kelvin’s the variable will be a ratio.
So. Numbers like 2, 3, 10, 10.5, Pi, etc. can be both interval or ratio, but you have to be careful with the context you are operating in.
Alright! We’ve quickly gone through the types of data and the measurement levels.
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Also known as a "Goodness of Fit" test, use this single sample Chi-Square test to determine if there is a significant difference between Observed and Expected values. This video shows a step-by-step method for calculating Chi-square.

Provides illustration of healthcare analytics using multinomial logistic regression and cardiotocographic data.
R file: https://goo.gl/ty2Jf2
Data: https://goo.gl/kMAh8U
Includes,
- steps for preparing data for the analysis
- use of nnet package in r
- calculation of probabilities using coefficients from the model
- estimating probabilities using the model
- developing confusion matrix
- calculation of misclassification error
Logistic regression is an important tool for developing classification or predictive analytics models related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

If you found this useful, look for my ebook on Amazon, Straightforward Statistics using Excel and Tableau.
Mutliple regression with a dummy variable as an independent variable. Uses Excel. Converts a categorical variables into a dummy coded [0,1] using Excel's =if() tool. Interpretation of estimated regression equation